Assignment One Nabila Binte Zahur.pdf

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Methods:
Data Collection
For our analysis we used the data on 2500 loans made by the Lending Club. The data was
downloaded from https://spark-public.s3.amazonaws.com/dataanalysis/loansData.rda on February,
16 2013 using the R programming language [4].

Exploratory Analysis
We conducted exploratory analysis by examining summaries of the loans data with plots and
tables. This was done in order to identify transformations to make on the raw data, and used
to remove a few fields with missing data and transform character/range data into factors or
numbers to simplify analysis. Following this, each of the variables in the original data were
plotted against the interest rate, using scatterplots and boxplots [5].

Statistical Modeling
In order to determine how important each of the remaining variables were in explaining the
interest rate, we performed a standard multivariate linear regression model, with coefficients
were estimated with ordinary least squares and standard errors were calculated using
standard asymptotic approximations [5]. The variables included in the regression model
were based on the exploratory analysis described above.

Results:
The loans data we analysed contained data on 14 variables for each loan made. These were
the interest rate, the FICO rating range, purpose of loan, length of the loan, amount of loan
requested, amount of loan funded by investors, monthly income, the Debt-to-Income Ratio,
number of open credit lines, amount of revolving credit, state, housing ownership status,
employment length and no. of past credit inquiries that had been made about the person in
the past 6 months.